Genetic Algorithms: What They Are and How To Build One

Genetic algorithms are a powerful tool for solving complex problems where there isn't an obvious solution or way to test different solutions. In this course you will learn the ins and outs of genetic algorithms, how they are built, what makes them tick, and how to design one for yourself. This is not just another lecture-based course where I teach you a bunch of theory and expect that you can implement it yourself. I go over every single step of creating a simple genetic algorithm from scratch in Python, demonstrating all of their elements as we build it to ensure that you understand how everything works together as a single unit. By the end of this course you will have developed a strong understanding about genetic algorithms, how they work and how to use them to build solutions for your own problems. By learning this technique you’ll have an edge over your competition as more companies start using algorithmic approaches even for simple decisions. This course is aimed at anyone who wants to understand, learn and apply simple genetic algorithms to solve problems for real-world applications. The course assumes prior experience with Python, and you will be able to immediately start applying the concepts learned in your own projects.

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00:00:00 Introduction
00:00:28 What is a "Genetic Algorithm"?
00:00:57 Gene Sequences
00:01:55 Benefits
00:02:53 Limitations
00:03:31 Possible Use Cases
00:04:05 Elements of Implementations
00:05:24 Steps of Implementations
00:07:28 Example Introduction
00:10:00 Item Class
00:13:12 Individual Class
00:16:15 Individual: Fitness Function
00:18:43 Individual: Single Point Crossover
00:22:24 Individual: Mutation
00:23:45 GeneticAlgorithm Class
00:25:00 GeneticAlgorithm: Initialize Population
00:28:55 GeneticAlgorithm: Select Best Individual
00:29:44 GeneticAlgorithm: Sum Values
00:30:35 GeneticAlgorithm: Select Parents
00:32:13 GeneticAlgorithm: Visual Generation
00:33:40 GeneticAlgorithm: Solve
00:42:03 Running / Testing
00:49:33 Alternative Crossovers Introduction
01:00:33 Alternative Crossovers: Two Point Crossover
01:05:45 Alternative Crossovers: Uniform Crossover
01:09:06 Alternative Crossovers: Sinusoidal Motion Crossover
01:12:43 Alternative Crossovers: Running Comparisons

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Please watch: "Rust API Documentation Made Easy - Swagger + Actix Web"
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[pablito]

I think you should change sorting to reverse in order_population function, because right now it is sorting from the lowest value to highest, but I think we want the highest value to be first, so that could improve the algortihm